A few works that focused on new isoforms of annotated genes, such as NANOG and FOXP1, have shown the importance of characterization of novel transcripts www.selleckchem.com/PD-1-PD-L1.html of PSCs. Thus, some researches, such as ENCODE project and Au’s work attempted to characterize the whole transcriptome of hESCs. Under a certain control of FRR, Au and his colleagues provided a list of novel genes and novel gene isoforms, from which a number of lncRNAs was predicted and their functions in pluripotency regulation were studied

as well. The previous works could not find these novel lncRNAs because of their highly repetitive sequences. Using the latest sequencing techniques, Au’s method overcame this difficulty. Therefore, more efforts are needed to expand and optimize this method to more PSCs, such as iPSC, the other hESC cell lines and embryo cells. As we complete transcriptome profiling of different PSCs and the transition stages between them, we will gain better understanding

of pluripotency. Papers of particular interest, published within the period of review, have been highlighted as: • of special interest “
“Current Opinion in Genetics & Development 2014, 28:78–82 This review comes from this website a themed issue on Cell reprogramming, regeneration and repair Edited by José CR Silva and Renee A Reijo Pera http://dx.doi.org/10.1016/j.gde.2014.09.010 0959-437X/© 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). The field of X chromosome inactivation (XCI), the process by which one X chromosome in female mammals is transcriptionally inactivated in order to equalize gene

expression in males and females, is now in its sixth decade and has produced a substantial understanding of the cell and molecular biology underlying this epigenetic regulation [1 and 2]. Even though our mechanistic understanding of the events in XCI is quite sophisticated, we are still identifying new players and further refining our understanding as illustrated by recent advances. With the discovery of induced pluripotent stem cells (iPSCs) in 2006 [3], a new subfield of XCI emerged to characterize X chromosome state in these cells and their derivatives. This new technology Ribonucleotide reductase made it possible to examine the same cells in a somatic context as well as an embryonic-like context to determine changes to the X chromosome during cell fate decisions, providing tools to interrogate reprogramming and pluripotency. This review will address new mechanistic advances in mouse and human XCI biology, the role of XCI in cancer initiation and progression, and new data on X chromosome state following reprogramming. Finally, it will discuss a new tool that has the ability to mark XCI in individual cells, which may be able to address many outstanding questions in the field.

As regards MeAV projections to the BST, it should be

noted that the tiny densely varicose subventricular foci (Figs. 3A, B, 6A) do not appear to correspond in location to the ejaculation-related clusters of Fos-immunorreactive neurons documented in rodents (Coolen et al., 1996 and Veening and Coolen, 1998), their functional implication being thus far unknown. Retrograde tracing observations in mice by Choi et al. (2005), in consonance with our own results, indicate that the MeAV innervates modestly the ventral premammillary nucleus and even more sparsely the medial preoptic nucleus. Having in mind that MeAV efferents also terminate rather modestly in the ventrolateral part of the ventromedial hypothalamic nucleus, and avoid almost completely the tuberal nucleus (present PHA-L observations), this scenario

suggest that the MeAV exerts little if any influence on key structures of a broad Trichostatin A nmr hypothalamic network subserving social behaviors (Motta et al., 2009, Newman, 1999, Simerly, 2002 and Swanson, 2000). Although Canteras and coworkers reliably Selleck ISRIB observed retrogradely labeled cells in ventral parts of the rat Me (including the MeAV) after injections in the dorsal premammillary nucleus (Comoli et al., 2000), they argued that this labeling probably reflects a spillover of the tracer into the ventral premammillary nucleus. The present PHA-L results indicating that the MeAV provides a clear input to the dorsal premammillary nucleus are in line with anterograde tracing studies (Gomez and Newman, 1992 and Luiten et al., 1985). A substantial retrograde labeling was noted in the MeAV after injections in the anterior hypothalamus (Choi et al., 2005 and Price et al., 1991; our own retrograde tracing experiments), however, the present anterograde tracing observations Protein kinase N1 suggest that this nucleus is essentially traversed

by labeled poorly varicose passing fibers. It should be noted that in the present study MeAV projections were examined in females, whether these projections are sexually dimorphic remain to be determined. Remarkably, in spite of the low density of receptors for gonadal hormones in the anterior Me (Simerly et al., 1990), variations in the volume of the MeAV were reported during the estrous cycle, probably related to changes in estradiol levels (Carrillo et al., 2007). The possible functional significance of the MeAV is discussed based on its connectivity and on insights from studies using the expression of immediate early genes, as markers of neuronal activity (Fig. 12). The MeAV receives robust projections from the main and accessory olfactory systems (Canteras et al., 1995, Kemppainen et al., 2002, Luskin and Price, 1983, Majak and Pitkänen, 2003, McDonald, 1998, Petrovich et al., 1996 and Savander et al., 1996; present observations) including direct projections from the main olfactory bulb (Kang et al., 2009, Pro-Sistiaga et al.

Therefore, for the purposes of this review, we will refer to the former set of areas collectively as ‘OFC’ and the latter, including medial OFC as ‘VMPFC’. However, we acknowledge that the information encoded and specific function of particular structures within these

general areas may have important differences [cf. 7]. There is general agreement from both single unit 10 and 11] and fMRI [12•] studies that parts of OFC encode the precise identity of rewards, and can represent specific associations between stimuli and economic parameters such as reward size, probability and delay 12•, 13 and 14]. Arguably the most reliable effect of disruption to this region is to reduce the influence of reinforcer devaluation on subsequent choices 15• and 16]. What

remains a matter XAV-939 price mTOR inhibitor of much debate is the function these signals play during learning and decision making. One possibility is this information is used to construct an integrated value signal that could underpin ‘goods-based’ decision making [4]. OFC represents the value of options (large negative < neutral < large positive) rather than their salience as defined by their divergence from indifference (large negative > neutral < large positive) 17 and 18]. However, the interpretation of such value coding has been challenged. Schoenbaum and colleagues have demonstrated in a series of elegant studies that cells in rat OFC are sensitive to parameters such as identity or associative why salience even when reward value is carefully controlled for 19 and 20]. Perhaps most compellingly, McDannald and colleagues [21••] recently showed that a population of OFC cells would increase

their firing when a new stimulus combination was followed by either an increase in reward magnitude or a different, but equally-preferred, flavour of reward. In fact, these cells would generally signal the degree of sensory and outcome divergence from the original learned state, a finding that chimes with several other studies showing rich, rapid sensory encoding in OFC 22 and 23]. Indeed, outside of the domain of reward quality and quantity, few OFC neurons encode combinations of economic parameters; instead, individual value parameters are encoded in overlapping small populations of neurons 13, 14, 24• and 25]. Given that OFC can encode information about the specific association between a stimulus and the sensory properties of a reward separate to any information about value, this implies that OFC’s role in the decision process is better described as the formation of stimulus-based predictions based on the attributes of rewards and the information to be gained from their outcomes, key inputs for a decision process. Another way of considering the functional significance of specific representations of expected outcomes is that they can facilitate appropriate updating of value estimates 6••, 26 and 27].

Many PIC government management institutions also currently invest substantial resources into culture-based restocking as a fishery management tool [46]. Conversely, the often weak capacity for analysing data to assess stocks, identifying processed products in trade, and inspecting dried sea cucumbers destined for export leads to two poor outcomes. Firstly, management agencies may have data on stock abundance and exports but struggle Quizartinib datasheet to analyse exploitation trends and, second, export data are not validated rigorously for imposing export levies. Financial and human resources of PIC management

agencies are very limiting [9] and long-term solutions to fisheries sustainability must arise from those finite resources. Redressing the inequalities in skill sets and weaknesses in management capacity will arguably require re-prioritisation of training needs within the management agencies and repartitioning of resources. In particular, some of the substantial resources

often allocated to developing marine reserves and culture-based restocking could be allocated to more active communication with fishers and engaging stakeholders in the management process. Resources could also be shifted from costly inspections U0126 at sea and underwater visual censuses to more cost-effective inspections of dried sea cucumbers on land, which would yield valuable data for regular re-diagnosis of stocks. The results show that the prioritisation of management objectives is fishery specific and/or manager specific. This is logical because the fisheries differ in the status of stocks and ecosystems, and some fisheries have been reserved for subsistence use. The top ranked objective reveals the perceived high importance of ecological resilience in the fisheries. Setting objectives is an important step in the management process [11] and [21] but seldom articulated for small-scale fisheries in the Pacific. Preston [9] found that conflict

between development objectives and EAF is the most common challenge for adopting EAF in Pacific Island fisheries. This may imply Org 27569 that management institutions must shift their conceptual focus from maximising profit and employment to a balance among yield, profit and ecosystem benefits while taking into account the needs of stakeholders [47]. The results also indicate that stock sustainability, environmental sustainability and socio-economic benefits are interrelated issues that cannot be easily separated in fisheries, especially in the context of an EAF. Managers should consider the ecosystem benefits of sea cucumbers, as they are known to contribute to nutrient recycling and ecosystem health on coral reefs (reviewed in [24] and [27]). That most managers ranked the subsistence-use objective low corresponds with the notion that sea cucumbers are an occasional food source in Pacific Islands [25] and food security does not depend directly on sustainability of sea cucumber fisheries.

Finally, one of the hallmarks of SLI is impairments of grammar, especially of rule-governed aspects of grammar (Bishop, 1997; for a detailed review of language problems in SLI see Leonard, 1998, Rice et al., 1998, Rice et al., 1999 and Ullman and Pierpont, Olaparib solubility dmso 2005).

Nevertheless, evidence suggests that declarative memory can at least partly compensate for these grammatical deficits in SLI, for example by storing complex forms as chunks, or learning explicit rules (Ullman and Pierpont, 2005). Other, non-procedural, functions that depend in part on the implicated procedural memory system brain structures also seem to show impairments in SLI (Ullman and Pierpont, 2005). Of interest here are reports of working memory impairments in the disorder (for reviews see Gathercole and Alloway, 2006 and Montgomery et al., 2010). Specifically, it has been found that children with SLI perform significantly more poorly on tasks requiring the short-term storage (Gathercole VX 770 and Baddeley, 1990) and processing of verbal information (Archibald and Gathercole, 2006b, Ellis

Weismer et al., 1999 and Marton and Schwartz, 2003). In contrast, visuo-spatial working memory has generally been reported to be spared in SLI (Alloway et al., 2009, Archibald and Gathercole, 2006a, Archibald and Gathercole, 2006b and Archibald and Gathercole, 2007). The reasons for this contrast between impaired verbal working memory and largely normal visuo-spatial working memory are not yet clear (see Discussion). The status of declarative memory in SLI has been examined in a limited number of studies. All studies that we are aware of have found IMP dehydrogenase normal learning in declarative memory for visual information (Baird et al., 2010, Bavin et al., 2005, Dewey and Wall, 1997, Lum et al., 2010, Riccio et al., 2007 and Williams et al., 2000). These tasks

have used a variety of paradigms that have been shown to depend on the declarative memory system (Lezak, 2004 and Ullman et al., 2008). For example, dot learning tasks, in which participants are asked to remember a set of randomly placed dots (Cohen, 1997), and which have been found to be impaired in SLI (Riccio et al., 2007), appear to depend at least in part on right medial temporal lobe structures (Brown et al., 2010). In contrast, the learning of verbal information in declarative memory has yielded a mixed pattern. (For simplicity, below we also refer to declarative memory for verbal information as verbal declarative memory, and likewise for visual declarative memory, and verbal and visuo-spatial working memory). Several studies have used list-learning paradigms. In this paradigm participants are typically presented with a list of words or word pairs, and are asked to orally recall the items immediately after each presentation, as well as following a short and/or long delay (Lezak, 2004).

Several thermophilic enzymes show indeed a remarkable stability at high temperatures, while others are unstable in pure preparations and obviously need the stabilizing capacity of cellular components. Tests at high temperatures are more complicated, not only due to more difficult thermostatting. Other components of the

assay mixture may become unstable and oxidation processes are accelerated. Besides the enzyme itself, substrates, co-substrates and cofactors5 are ATR inhibitor the most important components of the enzyme assay. Their state, their purity and stability is of particular importance and highest demands have to be made for these substances. With respect to the substrates a significant aspect must be considered. Usually it is taken that the enzyme has a defined substrate according to its physiological function, www.selleckchem.com/products/ABT-263.html as lactate dehydrogenase oxidizing lactate to pyruvate, or fumarase forming malate from fumarate. However, the substrate is not clearly defined in every case. Many enzymes show broad specificity, accepting also substances structurally related to the physiological substrate, like alcohol dehydrogenase, which reacts with various alcohols. The same holds for cofactors. Divalent cations are essential cofactors for many catalytic reactions

and they can often be substituted by other divalent cations. An interesting example is glucose isomerase, a microbial enzyme. Its physiological substrate is xylose, which becomes isomerized to xylulose with Mn2+ acting as essential cofactor. Due to its capacity to isomerize also glucose to the more valuable sugar fructose, the enzyme gained great interest in biotechnology. This non-physiological reaction proceeds more efficient with Co2+ than with Mn2+. So the change of the substrate causes also a change of the cofactor (Antrim et al., 1979 and Lehmacher and Bisswanger, 1990). In other cases the physiological substrate IMP dehydrogenase is replaced by an artificial, synthetic substrate, e.g. if the physiological substrate is unstable or, as in the case

of proteases, if the (protein) substrate is not well defined, rather the single peptide bond within the protein must be regarded as the genuine substrate. If the enzyme accepts different substrates, the question arises which substrate should be used for the enzyme assay? Due to the varying catalytic efficiency, results obtained for the same enzyme, but with different substrates, will hardly be comparable. The efficiency of a substrate is determined by its Km value, the lower this value the better the substrate. Usually the most efficient substrate may be taken, but also other aspects must be considered, like the availability, stability, solubility and the accessibility to a detection method. Sometimes natural substrates are modified to facilitate the detection. So it is not always the physiological substrate which is applied for the enzyme assay, but it is obvious that for comparison of the results the same substrate must always be used.

This paper assesses the annual dynamics of particulate organic matter concentrations in Baltic Proper seawater. Contemporary POC concentrations are modelled in the context of predicted increases in temperature and nutrient concentrations. Average values and increases of sea water nutrient concentrations, temperature and photosynthetically active radiation (PAR) recorded in the period 1965–1998 (Renk 2000) are used for evaluating realistic environmental conditions in the years to come. These factors have been selected as they are regarded as limiting

for phytoplankton primary production, thus influencing POC concentrations Alpelisib research buy directly and indirectly. Moreover, the rate of increase in these factors has already been quantified on the basis of actual observations (Renk 2000). The study concerns predictions for several areas of the southern Baltic Sea (Gdańsk Deep, Bornholm Deep and Gotland Deep). The biological part of the 1D CEM – Coupled Ecosystem Model (Dzierzbicka-Głowacka 2005, 2006), converted to a 1D POC – Particulate Organic Carbon Model with an

equation for dead organic matter (pelagic detritus), is presented in Dzierzbicka-Głowacka et al. (2010a) and Kuliński et al. (2011). The 1D POC model is an ecosystem model able to simulate the particulate organic carbon (POC) concentration as the sum of pelagic detritus and both phytoplankton and zooplankton biomass concentrations. In this model phytoplankton was modelled with the aid of only one state variable. The phytoplankton concentration was Chlormezanone taken to be a dynamically passive physical quantity, i.e. it was incapable of making autonomous movements. Cyanobacteria blooms

Raf kinase assay were not incorporated separately at this stage of the model development, so nitrogen fixation was ignored. The fact that cyanobacteria activity is less intense in the open sea than in the nearshore zone (Voss et al. 2005) provided additional motivation for choosing three stations located away from the coastal zone. Nutrients are represented by two components: total inorganic nitrogen (NO3− + NO2− + NH4+) and phosphate (PO43−). The temporal changes in the phytoplankton biomass are caused by primary production, excretion, mortality, grazing by zooplankton and sinking. The zooplankton biomass is affected by ingestion, excretion, faecal production, mortality and carnivorous grazing. The changes in the pelagic detritus concentration are determined by the input of dead phytoplankton and zooplankton, the natural mortality of predators, faecal pellets, and sinks – sedimentation, zooplankton grazing and decomposition (Dzierzbicka-Głowacka et al. 2010a). The zooplankton variable represents zooplankton of the first order. They ingest both phytoplankton and pelagic detritus – dead organic material in the model. The closure term of the model system is the carnivorous grazing of the zooplankton. The way the closure term is formulated sets up the behaviour of the model.

The Falklands and other southern Atlantic islands were developed for their squid fishery several years ago. You may be familiar with those satellite images of light at night, in which you will see that the Falklands squid fishery lights up almost as strongly as London or New York. The squid fishery is apparently in decline now, not surprisingly perhaps. However, to the fishing industry there is room for doubt: at one conference recently a fisheries expert admitted this decline but blamed… climate change! As one scientific colleague put it: “It is difficult

enough to get people to care about fish – what hope for squid!”. Another wasteful problem comes from the observation (Sloan, 2006) that by the end of a successful hunting trip, the bottom third of the tuna in some ships’ holds may be too squashed from the weight of fish above to be of much value. Some presumably Everolimus ic50 can be used for tinned cat food, but the rest is used as fertiliser for fields of crops. To an ecologist, the energetics implied by inputting a top carnivore into the base of a new food chain is astonishingly wasteful. Too much of this sort of profligacy could be the difference between collapse of a species or its survival, and between continuing revenue and benefit or its loss. It is only possible Selleck RG7204 because wild pelagic fish capture is more akin to clear-fell logging than to harvesting. Depressingly,

probably little on a global scale will Chlormezanone be done in time regarding management of multi-national fisheries over a multitude of EEZs. The literature on excesses of the blue water fishing fleet is huge, yet nothing much has happened. If proof is needed, just look at past decades of history and the trends of fishing intensity and fish stocks (Roberts, 2007). This applies even in the generally much more regulated European Union and its North Sea fishing industry. Wakefield (2009) recently reviewed this from a legal perspective and concluded that the situation is long past being supportable, and even the EU itself recently concluded that it has, in fact, messed up on a truly massive

scale. The fact is that we know the key facts, and have done so for many years, but facts are not enough. It is difficult to find examples where industrial fishing has succeeded without collapsing the stocks. Traditionally the fleets have just moved on: deeper, further offshore, but there are fewer and fewer places left. As has been pointed out for the whaling industry, from a company perspective it pays not to fish sustainably, but rather to maximise a return now, liquidate the asset and invest the earnings elsewhere, rather than to save some for later. In an analysis of 27 Scombrid stocks over half a century (mostly Atlantic and Pacific but with the only two Indian Ocean stocks for which there was sufficient data) Juan-Jorda et al.

After cooling, the cooled extract was centrifuged (5000 × g for 10 min) and then filtered through a Whatman no.5 filter paper. The extract was stored at −20 °C until analysed. The residual tissue was further digested with papain, and uronic acid contents in both the extract and the residual tissue were determined by the carbazole Baf-A1 in vivo reaction (see Section 2.7). These estimates enabled the proportion of uronic acid liberated to be expressed as a percentage of the total uronic acid recovered. The total extractability of uronic acid was then compared between the different extraction conditions. The preparation of each extract, which was referred to as antler papain extract, was performed in triplicate and the entire

experiment was independently replicated three times to address precision. Antler CS fractions were isolated and examined for molecular size using Sephacryl S-300 chromatography (Pharmacia Biotech Inc., Quebec, Canada). A portion of the antler papain extract was fractionated using a 1 × 110 cm column equilibrated and eluted with 0.05 M NaCl buffer, pH 5.8, at a flow rate of 3 mL/h. Blue dextran IDO inhibitor and tritiated water were used to determine void volume (Vo) and total volume (Vt) of the column, respectively. The partition coefficient was calculated from the formula: Kav = (Ve−Vo)/(Vt−Vo), in which Ve represents the volume of the peak fraction. The eluates (1 mL) were analysed for protein at 280 nm absorbance, hydroxyproline,

sulfated GAG and uronic acid content as explained in Section 2.7. Antler CS fractions were pooled MTMR9 and freeze-dried for further study. All chromatography data presented in this paper are means of 3 experiments. Electrophoresis was performed in 0.6% acrylamide in agarose in Tris buffer, pH 6.8. Samples were dissolved in deionised water. Two slabs were

generally run at the same time, one for staining with toluidine blue and the other for western blot with monoclonal antibodies to chondroitin sulfate (CS-56) (Sigma–Aldrich, USA). Electrophoretic transfer to nitrocellulose was accomplished in Tris–borate (gel electrophoresis buffer) without sodium dodecyl sulfate at 40 V for 2 h. Nitrocellulose sheets were then soaked in 2% bovine serum albumin (BSA) in phosphate-buffered saline (PBS), pH 7.2 for 1 h at room temperature. After washing in PBS (three times for 5 min each), nitrocellulose sheets were incubated with anti-CS monoclonal antibodies (in PBS containing 1% BSA) for 1 h at room temperature. The incubation was followed by washing in PBS and 1 h incubation with rabbit anti-mouse IgM conjugated with horseradish peroxidase. Colour was developed by incubating in 0.05% diaminobenzidine tetra-hydrochloride in PBS containing hydrogen peroxide (0.01% w/v) and cobalt chloride (0.033% w/v) for 5 min. Stained blots were then washed several times in water and dried. Hyaluronic acid from human umbilical cord (Sigma–Aldrich, USA) was dissolved in 0.5 M sodium acetate buffer, pH 6.

Calcd for C14H13Cl5N4Os∙H2O (1∙ H2O) (Mr = 622.79 g/mol): C, 27.00; H, 2.43; N, 9.00. Found: C, 27.41; H, 2.56; N, 8.85. ESI-MS in MeOH (negative): m/z 485 [OsIVCl5(Hind)]−, 367 [OsIVCl5]−, 333 [OsCl4]−. MIR, cm− 1: 603, 626, 664, 736, 784, 846, 872, 928, 978, 1077, 1136, 1181, 1238, 1309, 1382, 1441, 1505, 1584, 1618, 2348, 2933, 2975, 3135, 3487 and 3547. FIR, cm− 1: 159, 171, 203, 223, 283, 293, 308, 319, 350, 398, 427, 443, 537, 561 and 614.

UV–vis (H2O), λmax, nm (ε, M− 1 cm− 1): 288 (10 095), 362 (8 912), 406 sh (3 236), 560 (5 075), 598 (4 443). UV–vis (THF), λmax, nm (ε, M− 1 cm− 1): 367 (9 147), 408 sh (3 996), 518 (3 853), 593 (326). UV–vis (DMF), λmax, nm (ε, M− 1 cm− 1): 368 (10 000), 408 sh (3 949), 510 (4 080), 595 (251). UV–vis (DMSO), λmax, nm (ε, M− 1 cm− 1): 367 (5 687), 409 sh (2 752), 521 (2 794), Obeticholic Acid order 597 (304). 1H NMR (DMSO-d6, 500.32 MHz): δ − 14.54 (s, 1H, H3), − 0.43 (t, 1H, J = 7.67 Hz, H6), 2.81 (d, 1H, J = 8.56 Hz, H4), 4.52 (d, 1H, J = 8.83 Hz, H7), 6.66 (t, 1H, J = 6.91 Hz, H5), 7.11 (t, 1H, J = 7.19 Hz, H5′), 7.34 (t, 1H, J = 7.34 Hz, H6′), 7.54 (d, 1H, J = 8.42 Hz, H7′), 7.76 (d, 1H, J = 8.12 Hz, H4′), 8.07 (s, 1H, H3′), 14.25 (s, 1H, H2) ppm. 13C1H NMR (DMSO-d6, 125.82 MHz): I BET 762 δ 58.55 (C9), 99.06 (C7), 104.60 (C5), 110.56 (C7′),

120.67 (C5′), 120.98 (C4′), 123.22 (C9′), 126.41 (C6′), 133.82 (C3′), 140.32 (C8′), 157.09 (C4), 177.15 (C6), Amobarbital 184.29 (C8), 299.7 (C3) ppm. 15N NMR (DMSO-d6, 50.70 MHz): δ 85.9 (N2) ppm. Suitable crystals of 1·H2O for X-ray diffraction study were grown from a solution of 1 in DMSO. Analytical data for 2: ESI-MS in MeOH (negative): m/z 485 [OsIVCl5(Hind)]−, 367 [OsIVCl5]−. UV–vis (H2O), λmax, nm (ε, M− 1 cm− 1): 250 (11 134), 257 (10 982), 271 (10 841), 279 (11 395), 284 (11 751) 294 sh (9 593), 358 (8 882), 401 sh (4 770), 449 sh (2 411), 556 (669), 594 (632). UV–vis (THF), λmax, nm (ε, M− 1 cm− 1):

253 (10 264), 287 (12 955), 294 sh (11 844), 365 (9 728), ~ 510 sh (356). UV–vis (DMF), λmax, nm (ε, M− 1 cm− 1): 287 (15 146), 294 sh (13297), 366 (12 140), ~ 510 sh (244). UV–vis (DMSO), λmax, nm (ε, M− 1 cm− 1): 285 (11 680), 295 sh (9 562), 364 (8 249), 514 (503), 596 (51). UV–vis (MeOH), λmax, nm (ε, M− 1 cm− 1): 249 (9 450), 284 (12 152), 293 (10 019), 361 (8 780), 524 (562). 1H NMR (DMSO-d6, 500.32 MHz): δ − 4.54 (s, 1H, H3), 3.06 (t, 1H, J = 7.7 Hz, H6), 5.90 (d, 1H, J = 7.5 Hz, H4), 7.11 (t, 1H, J = 7.4 Hz, H5′), 7.34 (t, 1H, J = 7.6 Hz, H6′), 7.53 (d, 1H, J = 8.4 Hz, H7′), 7.76 (d, 1H, J = 8.1 Hz, H4′), 8.07 (s, 1H, H3′), 8.23 (t, 1H, J = 7.6 Hz, H5), 10.85 (d, 1H, J = 8.5 Hz, H7), 17.76 (s, 1H, H1) ppm.